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| Is Product Similarity a Blessing for Technological Acquisitions:A Perspective of Resource Orchestration |
| Wang Qian,Ren Shuming,Han Yueqi |
| (School of Economics and Management，Dalian University of Technology，Dalian 116024，China) |
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Abstract In recent years, technological innovation has gradually broken through corporate boundaries, and leading corporates have shifted from internal R&D to external markets to enhance innovation through technological acquisitions. Although the growth rate of technological acquisitions in terms of value and quantity has far outpaced the overall development of the whole M&A market, it has not had a significant positive impact on corporate innovation. Why, then, do Chinese corporates fail in technological acquisitions? Scholars have a long history of debate on this topic. Existing research has focused on the resource-based view of technological acquisitions from the perspective of resource complementarity, but ignored the dynamic theory and the mechanism of product similarity, which is exactly the key point of this paper.#br#This paper draws on the resource orchestration theory which treats the target selection stage as a resource structuring process and the post-merger integration stage as a resource bundling and a resource leveraging process, to explore the impact of product similarity on each stage of the technological acquisition. First, the resource orchestration theory defines the meaning of technological acquisitions and points out that technological acquisitions are dynamic processes of resource orchestration, and they involve structuring, bundling, and leveraging. This paper argues that resource orchestration theory can compensate for the shortcomings of the static analysis framework of the traditional resource-based view and provide new ideas for decisions on technological acquisitions. Second, the product is the result of dual feedback of technical resources, such as patents, research projects, and customer resources. When there is product similarity between two corporates, this similarity becomes an important basis for the selection of the target by the acquirer, and it is conducive to the improvement of the innovation level of the corporates and becomes the “blessing” of technological acquisitions.#br#As for methodology, this study first conducts a textual analysis to calculate product similarity between each pair of acquirers and targets by employing Python to extract data from annual reports of listed corporates. Then it applies a conditional Logit model as well as negative binomial regression for further econometric analyses. Results show that product similarity promotes structuring, that is, if corporates share high product similarity, it's easier to conduct technological acquisitions between each other; product similarity also contributes to bundling and leveraging through knowledge absorption channel to facilitate integration, while it makes a profit by leveraging the financing environment as well as market competition environment, which ultimately improves innovation. In general, acquirers can take product similarity as a reference for selecting targets when conducting technological acquisitions.#br#Compared with the previous literature, this paper makes innovations on the following three aspects. First, previous studies on resource orchestration theory are mainly focused on the qualitative and treat technological acquisitions as a dynamic process of resource orchestration, which is analyzed in three stages, namely, structuring, bundling, and leveraging. Second, this study analyzes the interrelationship between technology resources and customer resources behind product similarity and explores its impact on the innovation level of corporates after technological acquisitions. It thus expands the impact factors of technological acquisitions from the traditional resource complementarity perspective to the product similarity perspective, which can provide empirical evidence for the literature on technological acquisitions. Third, based on the text analysis method, the study uses Python to extract the textual information in the annual reports of Chinese A-shared listed corporates in the manufacturing industry, and constructs a corpus after obtaining the business fields to measure the product similarity values of both the acquirer and the target and interpret the product textual information in detail to improve the accuracy of product similarity measurement.#br#For policymakers, the government should strengthen the supervision of the information disclosure quality of listed corporates, build an environment of free competition towards corporate innovation, encourage enterprises to actively participate in technological acquisitions, and give play to the resource allocation effect of technological acquisitions on innovation.#br#
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Received: 08 September 2021
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